Contributors | Affiliation | Role |
---|---|---|
McGillicuddy, Dennis J. | Woods Hole Oceanographic Institution (WHOI) | Principal Investigator |
Bibby, Thomas | University of Southampton | Co-Principal Investigator |
Dinniman, Michael | Old Dominion University (ODU) | Co-Principal Investigator |
Greenan, Blair | Bedford Institute of Oceanography (BIO) | Co-Principal Investigator |
Hofmann, Eileen E. | Old Dominion University (ODU) | Co-Principal Investigator |
Klinck, John M. | Old Dominion University (ODU) | Co-Principal Investigator |
Sedwick, Peter N. | Old Dominion University (ODU) | Co-Principal Investigator |
Smith, Walker O. | Virginia Institute of Marine Science (VIMS) | Co-Principal Investigator |
Kosnyrev, Olga | Woods Hole Oceanographic Institution (WHOI) | Data Manager |
Biddle, Mathew | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Copley, Nancy | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
CTD data including nutrients, particulates, primary productivity, and trace metals from the Ross Sea collected in January and February, 2012
Hydrographic data and water samples were collected using a rosette sampler fitted with 24 10-L Niskin bottles (General Oceanics), an SBE 911 plus conductivity, temperature, and depth (CTD) sensors (SeaBird Electronics) and a WET Labs C-Star transmissometer (see additional instruments below). Nitrate and other macronutrient concentrations were measured at sea using standard autoanalyzer techniques. Seawater samples for trace metal analysis were collected with custom-modified 5-L Teflon-lined external-closure Niskin-X samplers (General Oceanics) on a trace-metal clean rosette deployed on a nonmetallic line, and dFe was determined post-cruise following the methods described by Sedwick et al. (2011).
Relevant References:
Sedwick, P. N. et al. Early season depletion of dissolved iron in the Ross Sea polynya: Implications for iron dynamics on the Antarctic continental shelf. Journal of Geophysical Research 116, C12019, doi:10.1029/2010jc006553 (2011).
2017-05-31 updates:
replaced version:2015-10-09. The original data is still the same but they expanded to include other parameters and made some data corrections.
1. 2 um Particulate TM data is added.
2. HPLC data is added.
3. Bottle file format in part of columns names location.
4. Several DATA CORRECTIONS were made in a process of merging
2015-10-09 updates:
replaced version:2014-04-11. The values are the same but the parameter names and the order of the columns was changed.
2014-04-11:
Original submission
1. Columns in data source:
2,4,6-9: Station info from CTD data files headers;
10: ISO_DateTime_UTC: added by DMO;
3,5,11-30: Sea-Bird SBE 9 CTD BTL data;
31-36: CTD Bottle Nutrients;
37-41: CTD Bottle biological data - Chlorophyll;
42-44: CTD Bottle biological data - Particulates;
45-47: CTD Bottle biological data - Primary productivity.
48-54: TMCTD stations info;
55-96: TMCTD Metals data
2. nd indicates not available data;
3. Flag -999.999 indicates “bad” data
CTD data was merged with TMCTD data at each station using CTD nominal depth and finding TMCTD data inside some depth interval depending on depth level. Nominal depth and location/time data for both instruments is included in the data.
File |
---|
bottle_NBP1201.csv (Comma Separated Values (.csv), 1.37 MB) MD5:cdd66c7028497079906b0a6d412fd47b Primary data file for dataset ID 511219 |
Parameter | Description | Units |
sta | station number | unitless |
depth_n | nominal depth | meters |
press | CTD pressure | decibars |
cast | CTD cast number | unitless |
bottle | CTD bottle number | unitless |
date | CTD date | yyyymmdd |
time | CTD time | hhmm |
lat | CTD latitude | decimal degrees |
lon | CTD longitude | decimal degrees |
ISO_DateTime_UTC | Date/Time (UTC) ISO formatted | YYYY-MM-DDTHH:MM:SS[xx]Z |
sal | salinity from primary sensor | unitless |
sal2 | salinity from secondary sensor | unitless |
density | sigma-theta density from primary sensor | kilograms/meter^3 |
density2 | sigma-theta density from secondary sensor | kilograms/meter^3 |
temp | temperature from primary sensor | degrees Celsius |
temp2 | temperature from secondary sensor | degrees Celsius |
cond | conductivity from primary sensor | Siemens/meter |
cond2 | conductivity from secondary sensor | Siemens/meter |
fluor | fluorescence | milligrams/m^3 |
trans | beam transmission | percent |
alt | altitude | meters |
par | PAR/Irradiance | microEinsteins/centimeter^2/second |
cpar | corrected Irradiance: CPAR = (100 * ratio multiplier * underwater PAR) / surface PAR where ratio multiplier = scaling factor used for comparing light fields of disparate intensity; input in .con file entry for surface PAR sensor. | microEinsteins/centimeter^2/second |
spar | SPAR/Surface Irradiance | microEinsteins/centimeter^2/second |
O2_v | oxygen voltage | volts |
O2_v2 | oxygen voltage; secondary sensor | volts |
potemp | potential temperature | degrees Celsius |
potemp2 | potential temperature; secondary sensor | degrees Celsius |
O2_ml_L1 | dissolved oxygen from CTD sensor | milliliters per liter |
O2_ml_L2 | dissolved oxygen from CTD secondary sensor | milliliters per liter |
bottle_nuts | CTD bottle number for nutrient analyses | unitless |
PO4 | Phosphate concentration | microMolar |
NO2 | nitrited concentration | microMolar |
NO2_NO3 | nitrate and nitrite concentration | microMolar |
NH4 | ammonium concentration | microMolar |
SiO4 | silicate concentration | microMolar |
Fo | fluorometric reading of non-adicified chlorophyll sample | unitless |
Fa | fluorometric reading of adicified chlorophyll sample | unitless |
Fo_Fa | ratio of chlorophyll-a to phaeopigment based on fluorometric readings of a non acidified (Fo) and acidified (Fa) samples | unitless |
chl_a | chlorophyll | micrograms/liter |
phaeo | total phaeopigment | micrograms/liter |
Bsi | biogenic Silica | microMolar |
PON | particulate organic Nitrogen | microMolar |
PIC | particulate organic Carbon | microMolar |
PP_L_hr | primary productivity | micrograms C/liter/hour |
PP_int_hr | integrated primary productivity per hour | micrograms C/square meter/hour |
PP_int_day | integrated primary productivity per day | micrograms C/square meter/day |
cast_tmctd | Trace Metal-CTD cast number | unitless |
lat_tmctd | Trace Metal-CTD latitude | decimal degrees |
lon_tmctd | Trace Metal-CTD longitude | decimal degrees |
bottle_tmctd | Trace Metal-CTD bottle number | unitless |
depth_tmctd | Trace Metal-CTD depth | meters |
date_tmctd | Trace Metal-CTDdate | yyyymmdd |
time_tmctd | Trace Metal-CTD time | hhmm |
dFe | dissolved Fe concentration | nanoMolar |
filter_code_0_4micron | filter code for 0.4 micron filter from TMCTD: 1=IC; 2=IE; 3=IR; 4=IF; 5=IG | unitless |
filter_id_0_4micron | filter number for 0.4 micron filter from TMCTD | unitless |
vol_filt_0_4micron | volume filtered for 0.4 micron filter from TMCTD | liters |
Mg_0_4micron | concentration of Magnesium in 0.4 micron filter from TMCTD | nanoMolar |
Mg_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Al_0_4micron | concentration of Aluminum in 0.4 micron filter from TMCTD | nanoMolar |
Al_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Si_0_4micron | concentration of Silica in 0.4 micron filter from TMCTD | nanoMolar |
Si_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
P_0_4micron | concentration of Phophorous in 0.4 micron filter from TMCTD | nanoMolar |
P_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
S_0_4micron | concentration of Sulfur in 0.4 micron filter from TMCTD | nanoMolar |
S_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Cl_0_4micron | concentration of Chlorine in 0.4 micron filter from TMCTD | nanoMolar |
Cl_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
K_0_4micron | concentration of Potassium in 0.4 micron filter from TMCTD | nanoMolar |
K_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Ca_0_4micron | concentration of Calcium in 0.4 micron filter from TMCTD | nanoMolar |
Ca_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Ti_0_4micron | concentration of Titanium in 0.4 micron filter from TMCTD | nanoMolar |
Ti_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
V_0_4micron | concentration of Vanadium in 0.4 micron filter from TMCTD | nanoMolar |
V_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Cr_0_4micron | concentration of Chromium in 0.4 micron filter from TMCTD | nanoMolar |
Cr_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Mn_0_4micron | concentration of Manganese in 0.4 micron filter from TMCTD | nanoMolar |
Mn_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Fe_P_0_4micron | concentration of particulate Iron in 0.4 micron filter from TMCTD | nanoMolar |
Fe_P_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Ni_0_4micron | concentration of Nickel in 0.4 micron filter from TMCTD | nanoMolar |
Ni_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Cu_0_4micron | concentration of Copper in 0.4 micron filter from TMCTD | nanoMolar |
Cu_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Zn_0_4micron | concentration of Zinc in 0.4 micron filter from TMCTD | nanoMolar |
Zn_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Br_0_4micron | concentration of Bromine in 0.4 micron filter from TMCTD | nanoMolar |
Br_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Sr_0_4micron | concentration of Strontium in 0.4 micron filter from TMCTD | nanoMolar |
Sr_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
Pb_0_4micron | concentration of Lead in 0.4 micron filter from TMCTD | nanoMolar |
Pb_3sd_0_4micron | 3 standard deviations from TMCTD | nanoMolar |
prim_prod | Primary Productivity | micrograms Carbon/Liter/Hour (ug C/L/h) |
prim_prod2 | Integrated Primary Productivity | miligrams Carbon/Meter^2/Hour (mg C/m^2/h) |
prim_prod3 | Integrated Primary Productivity | miligrams Carbon/Meter^2/Day (mg C/m^2/d) |
filter_code_2micron | filter code for 2 micron filter from TMCTD: 6=IM; 7=IN; 8=IO; 9=IP | unitless |
filter_id_2micron | filter number for 2 micron filter from TMCTD | unitless |
vol_filt_2micron | volume filtered for 2 micron filter from TMCTD | liters (L) |
Mg_2micron | concentration of Magnesium in 2 micron filter from TMCTD | nanoMolar |
Mg_err_2micron | error from TMCTD | nanoMolar |
Al_2micron | concentration of Aluminum in 2 micron filter from TMCTD | nanoMolar |
Al_err_2micron | error from TMCTD | nanoMolar |
Si_2micron | concentration of Silica in 2 micron filter from TMCTD | nanoMolar |
Si_err_2micron | error from TMCTD | nanoMolar |
P_2micron | concentration of Phophorous in 2 micron filter from TMCTD | nanoMolar |
P_err_2micron | error from TMCTD | nanoMolar |
S_2micron | concentration of Sulfur in 2 micron filter from TMCTD | nanoMolar |
S_err_2micron | error from TMCTD | nanoMolar |
Cl_2micron | concentration of Chlorine in 2 micron filter from TMCTD | nanoMolar |
Cl_err_2micron | error from TMCTD | nanoMolar |
K_2micron | concentration of Potassium in 2 micron filter from TMCTD | nanoMolar |
K_err_2micron | error from TMCTD | nanoMolar |
Ca_2micron | concentration of Calcium in 2 micron filter from TMCTD | nanoMolar |
Ca_err_2micron | error from TMCTD | nanoMolar |
Ti_2micron | concentration of Titanium in 2 micron filter from TMCTD | nanoMolar |
Ti_err_2micron | error from TMCTD | nanoMolar |
V_2micron | concentration of Vanadium in 2 micron filter from TMCTD | nanoMolar |
V_err_2micron | error from TMCTD | nanoMolar |
Cr_2micron | concentration of Chromium in 2 micron filter from TMCTD | nanoMolar |
Cr_err_2micron | error from TMCTD | nanoMolar |
Mn_2micron | concentration of Manganese in 2 micron filter from TMCTD | nanoMolar |
Mn_err_2micron | error from TMCTD | nanoMolar |
Fe_P_2micron | concentration of particulate Iron in 2 micron filter from TMCTD | nanoMolar |
Fe_P_err_2micron | error from TMCTD | nanoMolar |
Ni_2micron | concentration of Nickel in 2 micron filter from TMCTD | nanoMolar |
Ni_err_2micron | error from TMCTD | nanoMolar |
Cu_2micron | concentration of Copper in 2 micron filter from TMCTD | nanoMolar |
Cu_err_2micron | error from TMCTD | nanoMolar |
Zn_2micron | concentration of Zinc in 2 micron filter from TMCTD | nanoMolar |
Zn_err_2micron | error from TMCTD | nanoMolar |
Br_2micron | concentration of Bromine in 2 micron filter from TMCTD | nanoMolar |
Br_err_2micron | error from TMCTD | nanoMolar |
Sr_2micron | concentration of Strontium in 2 micron filter from TMCTD | nanoMolar |
Sr_err_2micron | error from TMCTD | nanoMolar |
Pb_2micron | concentration of Lead in 2 micron filter from TMCTD | nanoMolar |
Pb_err_2micron | error from TMCTD | nanoMolar |
hplc_sample_num | HPLC sample number | unitless |
hplc_tot_vol_mL | HPLC volume filtered seawater | mililiters (mL) |
hplc_tot_vol_L | HPLC volume filtered seawater | Liters (L) |
hplc_vol_90pcnt_acetone | volume of 90 pcnt acetone | microliters (uL) |
hplc_vol_CTX | volume of canthaxanthin | microliters (uL) |
hplc_vol_extract_uL | total volume extraction (acetone + cantha) (uL) | microliters (uL) |
hplc_vol_extract_mL | Vext: total volume extraction (acetone + cantha) | mililiters (mL) |
hplc_inject_vol_sample | for injection: volume sample | microliters (uL) |
hplc_buff_vol | volume buffer | microliter (uL) |
hplc_samp_buff_vol | sample + buffer volume | microliter (uL) |
hplc_vol_inj_samp_bot | Vinj: vol injected sample + buffer | microliter (uL) |
hplc_buff_dil_factor | B: buffer dilution factor | unitless |
hplc_chl_c3 | chlorophyll c3 (RT = 6.200) pick area (Ap) | unitless |
hplc_peri | peridinin (partial peak) (see Notes Worksheet) (RT = 9.783) pick area (Ap) | unitless |
hplc_but19 | 19-butanoyloxyfucoxanthin (RT = 10.850) pick area (Ap) | unitless |
hplc_fuco | fucoxanthin (RT = 10.867) pick area (Ap) | unitless |
hplc_hex19 | 19hex (RT = 11.633) pick area (Ap) | unitless |
hplc_allo | allo (RT 15.3 min) (RT = 15.950) pick area (Ap) | unitless |
hplc_cantha | canthaxanthine (RT = 18.150) pick area (Ap) | unitless |
hplc_chla_allo | chla allo pick area (Ap) | unitless |
hplc_chla | chla (RT = 23.316) pick area (Ap) | unitless |
hplc_chla_epi | chlorophyll a epimer pick area (Ap) | unitless |
hplc_chla_sum | sum chla pick area (Ap) | unitless |
hplc_chl_c3_slope | Slope from calibration; total pigment in injected sample; 8945657: chlorophyll c3 | micrograms (ug) |
hplc_peri_slope | Slope from calibration; total pigment in injected sample; 5631629: peridinin | micrograms (ug) |
hplc_but19_slope | Slope from calibration; total pigment in injected sample; 8477663: 19-butanoyloxyfucoxanthin | micrograms (ug) |
hplc_fuco_slope | Slope from calibration; total pigment in injected sample; 8795122: fucoxanthin | micrograms (ug) |
hplc_hex19_slope | Slope from calibration; total pigment in injected sample; 9134242: 19hex | micrograms (ug) |
hplc_allo_slope | Slope from calibration; total pigment in injected sample; 10983522: allo | micrograms (ug) |
hplc_cantha_slope | Slope from calibration; total pigment in injected sample; 8809622: canthaxanthine (red = too low or too high; see Notes worksheet) | micrograms (ug) |
hplc_chla_slope | Slope from calibration; total pigment in injected sample; 2148822: chla | micrograms (ug) |
hplc_jeff_chl_c3 | Jeffrey chlorophyll c3 | nanograms/Liter (ng/L) |
hplc_jeff_peri | Jeffrey peridinin | nanograms/Liter (ng/L) |
hplc_jeff_19but | Jeffrey 19-butanoyloxyfucoxanthin | nanograms/Liter (ng/L) |
hplc_jeff_fuco | Jeffrey fucoxanthin | nanograms/Liter (ng/L) |
hplc_jeff_19hex | Jeffrey 19hex | nanograms/Liter (ng/L) |
hplc_jeff_allo | Jeffrey allo | nanograms/Liter (ng/L) |
hplc_jeff_cantha | Jeffrey canthaxanthine | nanograms/Liter (ng/L) |
hplc_jeff_chla | Jeffrey chla | nanograms/Liter (ng/L) |
hplc_fluor_chl_a | fluor Chl a | micrograms/Liter (ug/L) |
hplc_fluor_phaeo_a | fluor phaeo a | micrograms/Liter (ug/L) |
Dataset-specific Instrument Name | Altimeter |
Generic Instrument Name | Altimeter |
Generic Instrument Description | An instrument that measures height above a fixed surface. The data can be used to map ocean-surface topography and generate gridded surface height fields. |
Dataset-specific Instrument Name | CTD SBE 911plus |
Generic Instrument Name | CTD Sea-Bird SBE 911plus |
Generic Instrument Description | The Sea-Bird SBE 911 plus is a type of CTD instrument package for continuous measurement of conductivity, temperature and pressure. The SBE 911 plus includes the SBE 9plus Underwater Unit and the SBE 11plus Deck Unit (for real-time readout using conductive wire) for deployment from a vessel. The combination of the SBE 9 plus and SBE 11 plus is called a SBE 911 plus. The SBE 9 plus uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3 plus and SBE 4). The SBE 9 plus CTD can be configured with up to eight auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorescence, light (PAR), light transmission, etc.). more information from Sea-Bird Electronics |
Dataset-specific Instrument Name | HPLC |
Generic Instrument Name | High-Performance Liquid Chromatograph |
Dataset-specific Description | High Performance Liquid Chromatograph |
Generic Instrument Description | A High-performance liquid chromatograph (HPLC) is a type of liquid chromatography used to separate compounds that are dissolved in solution. HPLC instruments consist of a reservoir of the mobile phase, a pump, an injector, a separation column, and a detector. Compounds are separated by high pressure pumping of the sample mixture onto a column packed with microspheres coated with the stationary phase. The different components in the mixture pass through the column at different rates due to differences in their partitioning behavior between the mobile liquid phase and the stationary phase. |
Dataset-specific Instrument Name | LI-COR Biospherical PAR |
Generic Instrument Name | LI-COR Biospherical PAR Sensor |
Generic Instrument Description | The LI-COR Biospherical PAR Sensor is used to measure Photosynthetically Available Radiation (PAR) in the water column. This instrument designation is used when specific make and model are not known. |
Dataset-specific Instrument Name | Niskin bottle |
Generic Instrument Name | Niskin bottle |
Dataset-specific Description | Rosette fitted with 24 10-L General Oceanics Niskin bottles |
Generic Instrument Description | A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. |
Dataset-specific Instrument Name | Niskin-1010X |
Generic Instrument Name | Niskin-1010X |
Dataset-specific Description | Custom-modified 5-L Teflon-lined external-closure Niskin-X samplers (General Oceanics) on a trace-metal clean rosette deployed on a nonmetallic line. |
Generic Instrument Description | The Model 1010X NISKIN-X External Spring Niskin Water Sampler is a Niskin water sample bottle with the stainless steel closure springs mounted externally. The external closure mechanism is designed to support applications such as trace metal analysis where the inside of the sampler must be totally free of contaminants. The 1010X Niskin bottle, manufactured by General Oceanics Inc., is available in a variety of sizes (sample volume). It can be activated by the GO Devil Messenger (1000-MG) if individually or serially attached to a hydrocable or can be deployed as part of a Rosette multibottle array. The bottles can be teflon-lined and are available as GO-FLO bottles to further avoid sample contamination. (more from General Oceanics) |
Dataset-specific Instrument Name | SBE-43 DO |
Generic Instrument Name | Sea-Bird SBE 43 Dissolved Oxygen Sensor |
Generic Instrument Description | The Sea-Bird SBE 43 dissolved oxygen sensor is a redesign of the Clark polarographic membrane type of dissolved oxygen sensors. more information from Sea-Bird Electronics |
Dataset-specific Instrument Name | Transmissometer |
Generic Instrument Name | Transmissometer |
Dataset-specific Description | Chelsea/Seatech transmissometer |
Generic Instrument Description | A transmissometer measures the beam attenuation coefficient of the lightsource over the instrument's path-length. This instrument designation is used when specific manufacturer, make and model are not known. |
Dataset-specific Instrument Name | ECO AFL/FL |
Generic Instrument Name | Wet Labs ECO-AFL/FL Fluorometer |
Generic Instrument Description | The Environmental Characterization Optics (ECO) series of single channel fluorometers delivers both high resolution and wide ranges across the entire line of parameters using 14 bit digital processing. The ECO series excels in biological monitoring and dye trace studies. The potted optics block results in long term stability of the instrument and the optional anti-biofouling technology delivers truly long term field measurements.
more information from Wet Labs |
Website | |
Platform | RVIB Nathaniel B. Palmer |
Report | |
Start Date | 2011-12-24 |
End Date | 2012-02-11 |
Description | From McMurdo Station to Punta Arenas, Chile
More information from R2R: https://www.rvdata.us/search/cruise/NBP1201 |
The NSF proposal title was "Impact of Mesoscale Processes on Iron Supply and Phytoplankton Dynamics in the Ross Sea"
The Ross Sea continental shelf is one of the most productive areas in the Southern Ocean, and may comprise a significant, but unaccounted for, oceanic CO2 sink, largely driven by phytoplankton production. The processes that control the magnitude of primary production in this region are not well understood, but data suggest that iron limitation is a factor. Field observations and model simulations indicate four potential sources of dissolved iron to surface waters of the Ross Sea: (1) circumpolar deep water intruding from the shelf edge; (2) sediments on shallow banks and nearshore areas; (3) melting sea ice around the perimeter of the polynya; and (4) glacial meltwater from the Ross Ice Shelf. The principal investigators hypothesize that hydrodynamic transport via mesoscale currents, fronts, and eddies facilitate the supply of dissolved iron from these four sources to the surface waters of the Ross Sea polynya. These hypotheses will be tested through a combination of in situ observations and numerical modeling, complemented by satellite remote sensing. In situ observations will be obtained during a month-long cruise in the austral summer. The field data will be incorporated into model simulations, which allow quantification of the relative contributions of the various hypothesized iron supply mechanisms, and assessment of their impact on primary production. The research will provide new insights and a mechanistic understanding of the complex oceanographic phenomena that regulate iron supply, primary production, and biogeochemical cycling. The research will thus form the basis for predictions about how this system may change in a warming climate. The research will contribute to the goals of the international research programs ICED (Integrated Climate and Ecosystem Dynamics) and GEOTRACES (Biogeochemical cycling and trace elements in the marine environment).
The Ocean Carbon and Biogeochemistry (OCB) program focuses on the ocean's role as a component of the global Earth system, bringing together research in geochemistry, ocean physics, and ecology that inform on and advance our understanding of ocean biogeochemistry. The overall program goals are to promote, plan, and coordinate collaborative, multidisciplinary research opportunities within the U.S. research community and with international partners. Important OCB-related activities currently include: the Ocean Carbon and Climate Change (OCCC) and the North American Carbon Program (NACP); U.S. contributions to IMBER, SOLAS, CARBOOCEAN; and numerous U.S. single-investigator and medium-size research projects funded by U.S. federal agencies including NASA, NOAA, and NSF.
The scientific mission of OCB is to study the evolving role of the ocean in the global carbon cycle, in the face of environmental variability and change through studies of marine biogeochemical cycles and associated ecosystems.
The overarching OCB science themes include improved understanding and prediction of: 1) oceanic uptake and release of atmospheric CO2 and other greenhouse gases and 2) environmental sensitivities of biogeochemical cycles, marine ecosystems, and interactions between the two.
The OCB Research Priorities (updated January 2012) include: ocean acidification; terrestrial/coastal carbon fluxes and exchanges; climate sensitivities of and change in ecosystem structure and associated impacts on biogeochemical cycles; mesopelagic ecological and biogeochemical interactions; benthic-pelagic feedbacks on biogeochemical cycles; ocean carbon uptake and storage; and expanding low-oxygen conditions in the coastal and open oceans.
The BCO-DMO database includes data from IMBER endorsed projects lead by US funded investigators. There is no dedicated US IMBER project or data management office. Those functions are provided by US-OCB and BCO-DMO respectively.
The information in this program description pertains to the Internationally coordinated IMBER research program. The projects contributing data to the BCO-DMO database are those funded by US NSF only. The full IMBER data catalog is hosted at the Global Change Master Directory (GCMD).
IMBER Data Portal: The IMBER project has chosen to create a metadata portal hosted by the NASA's Global Change Master Directory (GCMD). The GCMD IMBER data catalog provides an overview of all IMBER endorsed and related projects and links to datasets, and can be found at URL http://gcmd.nasa.gov/portals/imber/.
IMBER research will seek to identify the mechanisms by which marine life influences marine biogeochemical cycles, and how these, in turn, influence marine ecosystems. Central to the IMBER goal is the development of a predictive understanding of how marine biogeochemical cycles and ecosystems respond to complex forcings, such as large-scale climatic variations, changing physical dynamics, carbon cycle chemistry and nutrient fluxes, and the impacts of marine harvesting. Changes in marine biogeochemical cycles and ecosystems due to global change will also have consequences for the broader Earth System. An even greater challenge will be drawing together the natural and social science communities to study some of the key impacts and feedbacks between the marine and human systems.
To address the IMBER goal, four scientific themes, each including several issues, have been identified for the IMBER project: Theme 1 - Interactions between Biogeochemical Cycles and Marine Food Webs; Theme 2 - Sensitivity to Global Change: How will key marine biogeochemical cycles, ecosystems and their interactions, respond to global change?; Theme 3 - Feedback to the Earth System: What are the roles of the ocean biogeochemistry and ecosystems in regulating climate?; and Theme 4 - Responses of Society: What are the relationships between marine biogeochemical cycles, ecosystems, and the human system?
Funding Source | Award |
---|---|
NSF Antarctic Sciences (NSF ANT) |